"""
============
Contour Demo
============

Illustrate simple contour plotting, contours on an image with
a colorbar for the contours, and labelled contours.

See also the :doc:`contour image example
</gallery/images_contours_and_fields/contour_image>`.
"""
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt


delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2

###############################################################################
# Create a simple contour plot with labels using default colors.  The
# inline argument to clabel will control whether the labels are draw
# over the line segments of the contour, removing the lines beneath
# the label

fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z)
ax.clabel(CS, inline=1, fontsize=10)
ax.set_title('Simplest default with labels')


###############################################################################
# contour labels can be placed manually by providing list of positions
# (in data coordinate). See ginput_manual_clabel.py for interactive
# placement.

fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z)
manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]
ax.clabel(CS, inline=1, fontsize=10, manual=manual_locations)
ax.set_title('labels at selected locations')


###############################################################################
# You can force all the contours to be the same color.

fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z, 6,
                 colors='k',  # negative contours will be dashed by default
                 )
ax.clabel(CS, fontsize=9, inline=1)
ax.set_title('Single color - negative contours dashed')

###############################################################################
# You can set negative contours to be solid instead of dashed:

matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z, 6,
                 colors='k',  # negative contours will be dashed by default
                 )
ax.clabel(CS, fontsize=9, inline=1)
ax.set_title('Single color - negative contours solid')


###############################################################################
# And you can manually specify the colors of the contour

fig, ax = plt.subplots()
CS = ax.contour(X, Y, Z, 6,
                 linewidths=np.arange(.5, 4, .5),
                 colors=('r', 'green', 'blue', (1, 1, 0), '#afeeee', '0.5')
                 )
ax.clabel(CS, fontsize=9, inline=1)
ax.set_title('Crazy lines')


###############################################################################
# Or you can use a colormap to specify the colors; the default
# colormap will be used for the contour lines

fig, ax = plt.subplots()
im = ax.imshow(Z, interpolation='bilinear', origin='lower',
                cmap=cm.gray, extent=(-3, 3, -2, 2))
levels = np.arange(-1.2, 1.6, 0.2)
CS = ax.contour(Z, levels, origin='lower', cmap='flag',
                linewidths=2, extent=(-3, 3, -2, 2))

# Thicken the zero contour.
zc = CS.collections[6]
plt.setp(zc, linewidth=4)

ax.clabel(CS, levels[1::2],  # label every second level
          inline=1, fmt='%1.1f', fontsize=14)

# make a colorbar for the contour lines
CB = fig.colorbar(CS, shrink=0.8, extend='both')

ax.set_title('Lines with colorbar')

# We can still add a colorbar for the image, too.
CBI = fig.colorbar(im, orientation='horizontal', shrink=0.8)

# This makes the original colorbar look a bit out of place,
# so let's improve its position.

l, b, w, h = ax.get_position().bounds
ll, bb, ww, hh = CB.ax.get_position().bounds
CB.ax.set_position([ll, b + 0.1*h, ww, h*0.8])

plt.show()

#############################################################################
#
# ------------
#
# References
# """"""""""
#
# The use of the following functions and methods is shown
# in this example:

import matplotlib
matplotlib.axes.Axes.contour
matplotlib.pyplot.contour
matplotlib.figure.Figure.colorbar
matplotlib.pyplot.colorbar
matplotlib.axes.Axes.clabel
matplotlib.pyplot.clabel
matplotlib.axes.Axes.set_position
matplotlib.axes.Axes.get_position
